Representing, Matching, and Generalising Structural Descriptions of Complex Physical Objects

This thesis addresses the problem of representing, matching, and generalising descriptions of complex structured physical objects, in the absence of functional and domain-specific knowledge. A system called GRAM is described, which includes a representation scheme, an instance-constructor, a matcher, and a generaliser. These components incorporate and extend ideas from a number of other structured-object learning systems, as well as introducing several new ideas. A central contribution of this thesis is to show that descriptions of complex physical objects can be matched and generalised effectively and efficiently by exploiting their structure. GRAM does this by a number of means, such as by representing objects at multiple levels of detail; using 'neighbour relationships' to allow a more flexible traversal of object graphs during matching; explicitly distinguishing between substructure and context to allow partial matching and a simple form of disjunction; and using an explicit representation of groups to describe several similar objects as a single descriptive entity. A second contribution is to show that complex objects can be matched without having to enforce consistency between object correspondences. This is possible partly because of the richness of physical objects, and partly because GRAM represents concepts as simple entities defined by relationships with other concepts, rather than as a complete set of subcomponents defined locally within the concept description itself. This scheme leads to greater simplicity, efficiency, and robustness

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Representing, Matching, and Generalising Structural Descriptions of Complex Physical Objects

This thesis addresses the problem of representing, matching, and generalising descriptions of complex structured physical objects, in the absence of functional and domain-specific knowledge. A system called GRAM is described, which includes a representation scheme, an instance-constructor, a matcher, and a generaliser. These components incorporate and extend ideas from a number of other structured-object learning systems, as well as introducing several new ideas. A central contribution of this thesis is to show that descriptions of complex physical objects can be matched and generalised effectively and efficiently by exploiting their structure. GRAM does this by a number of means, such as by representing objects at multiple levels of detail; using 'neighbour relationships' to allow a more flexible traversal of object graphs during matching; explicitly distinguishing between substructure and context to allow partial matching and a simple form of disjunction; and using an explicit representation of groups to describe several similar objects as a single descriptive entity. A second contribution is to show that complex objects can be matched without having to enforce consistency between object correspondences. This is possible partly because of the richness of physical objects, and partly because GRAM represents concepts as simple entities defined by relationships with other concepts, rather than as a complete set of subcomponents defined locally within the concept description itself. This scheme leads to greater simplicity, efficiency, and robustness.</p

Effect of Antiplatelet Therapy on Survival and Organ Support–Free Days in Critically Ill Patients With COVID-19

International audienc

Methods for reactive oxygen species (ROS) detection in aqueous environments

This review summarizes direct and indirect analytical methods for the detection and quantification of the reactive oxygen species (ROS): 1O2, O2·−/HOO·, H2O2, HO·, and CO3·− in aqueous solution. Each section briefly describes the chemical properties of a specific ROS followed by a table (organized alphabetically by detection method, i.e., absorbance, chemiluminescence, etc.) summarizing the nature of the observable (associated analytical signal) for each method, limit of detection, application notes, and reaction of the probe molecule with the particular ROS